Fully Homomorphic Encryption for Statistical Modeling

2026-10-03

AI Summary

This study addresses the privacy conflict between non-shareable data and joint statistical analysis in multi-party collaborations. Leveraging the OpenFHE library (CKKS/BFV schemes), this work pioneers the seamless integration of fully homomorphic encryption (FHE) into R-based statistical workflows by proposing an encrypted composition paradigm grounded in function value bounds. By combining consensus ADMM with threshold key generation, it enables secure statistical modeling and multiparty computation directly over ciphertexts. The proposed framework is successfully validated through distributed Cox regression and federated learning models, ensuring complete data privacy throughout the analytical pipeline. Ultimately, this research delivers an efficient and practical toolchain within the R ecosystem for privacy-preserving computation.

Abstract

Fully homomorphic encryption allows arithmetic to be carried out on encrypted values, so that a party performing a computation need not see the data it operates on. This is useful wherever collaborating parties or sites cannot share data records or computed summaries yet need to do a joint analysis. We demonstrate the protocols needed to perform such analyses reproducibly via two packages in R, a platform widely used for applied statistics. The first, openfhe.R, is an interface to the OpenFHE C++ library, which exposes exact integer arithmetic (BFV, BGV), approximate real-valued arithmetic (CKKS), Boolean circuits, and multiparty key generation. The second is homomorpheR, which builds a small set of master/worker primitives for multi-party protocols on top of the first. Together, the two let an ordinary R statistical routine compose with an encrypted-arithmetic layer at the function-value boundary, provided the quantity crossing that boundary decomposes as a sum over sites. Two validation studies are presented: distributed stratified Cox regression, where stats4::mle() converges through an encrypted channel, and federated Cox-lasso via convex optimization using consensus ADMM, where the cross-site update is the only encrypted step. We also discuss federated similarity retrieval and two-party prediction. Throughout, the parties are assumed honest-but-curious, and threshold key generation removes the assumption that any single party holds a usable secret key.

Problem

• Fully Homomorphic Encryption
• Statistical Modeling
• Federated Analysis
• Privacy-preserving Computation
• Multi-party Protocols

Innovation

• Fully Homomorphic Encryption
• Statistical Modeling
• Federated Learning
• R packages
• Threshold Key Generation